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082			_a001.4226028566 AC _223
100	1		_aAllchin, Carl, _eauthor.
245			_aTableau Prep : _bup and running : self-service data preparation for better analysis / _cCarl Allchin.
250			_a1st ed.
260			_bO'Reilly, _c2020. _aNew Delhi :
300			_axxvi, 415 p. : _billustrations (black and white) ; _c24 cm.
500			_aIncludes index.
505			_aPreface Why I Wrote This Book Who This Book Is For How This Book Is Organized Acknowledgments Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us 1. Why Self-Service Data Prep? A Short History of Self-Service Data Visualization Accessing the “Right Data” The Self-Service Data Preparation Opportunity Tableau Prep Up and Running Summary I. Getting Started 2. Getting Started with Tableau Prep Builder Where to Get Tableau Prep Builder How to Get a License for Prep Builder The Tableau Prep Builder Screen Basic Steps of Data Preparation Input Step Clean Step Output Step Saving a Flow Summary 3. Planning Your Prep Stage 1: Know Your Data Stage 2: Identify the Desired State Stage 3: Determine the Required Transitions from KYD to the Desired State Stage 4: Build the Workflow Summary 4. Shaping Data What to Look for in Incoming Data Sets What Shape Is Best for Analysis in Tableau? Changing Data Set Structures in Prep Builder Pivot Aggregate Join Union Applying Restructuring Techniques to the Ice Cream Example Step 1: Pivot Columns to Rows Step 2: Pivot Rows to Columns Summary 5. Connecting to Data in Files Files Upon Files Upon Files Spreadsheets Other File Types Where to Find Your Data Files How to Connect to Files in Prep Considerations for Saving Flows with File Inputs Summary 6. Connecting to a Database What Is a Database? How to Connect to a Database Within Prep Builder When to Avoid Connecting to a Database Summary II. Data Types 7. Dealing with Numbers What Do We Mean by Numbers? Types of Numbers Category or Measure? Aggregation Formatting Numbers Functions for Mastering Numerical Data Summary 8. Dealing with Dates Why Are Dates Important? Parts of a Date Date Lookup Tables Epoch Dates Excel Serial Number Entering Dates The makedate() Function The dateparse() Function Summary 9. Dealing with String Data What Do We Mean by Strings? How String Data Is Different Character Order Formatting Considerations Common Functions for Preparing String Data Grouping and Replace Options for Working with String Data Summary 10. Dealing with Boolean Data What Is Boolean Data? Why Is It So Useful in Data Analysis? Functions Featuring Boolean Logic Summary III. The Shape of Data 11. Profiling Data What Is a Profile? Why Visualizing the Data Set Is Important Anscombe’s Quartet Visualizations Versus Data Tables How Prep Builder Profiles Data Generating Histograms and Mini-Histograms Selecting Summary Versus Detail Views Highlighting Values Viewing Dimension Counts Sorting Summary 12. Sampling Data Sets One Simple Rule: Use It All If Possible Sampling to Work Around Technical Limitations Volume of Data Velocity of Data Other Reasons for Sampling Reduce Build Times Determine What You Need Sampling Techniques Fixed Number of Rows Random Sample When Not to Sample Summary 13. Pivoting Columns to Rows When to Pivot in Tableau Prep Builder How to Pivot Columns to Rows Summary 14. Pivoting Rows to Columns When to Use a Rows-to-Columns Pivot How to Pivot Rows to Columns Summary 15. Aggregating in Prep Builder Comparing Calculations in Prep Builder and Desktop Which Calculations in Prep Builder Differ? Adding the Aggregate Step Where’s the Rest of My Data? Level of Detail Calculation Option Summary 16. Joining Data Sets Together How to Join Data Sets in Prep Builder Join Logic and Terminology Types of Join in Prep Builder When to Use Each Join Type Summary 17. Unioning What Is a Union? What If the Data Structure Isn’t Identical? When to Union Data Monthly Data Sets Data Sets from Web Sources Company Mergers Multiple Tables and Wildcard Unions Summary 18. Calculations What Do Calculations Do in Data Preparation? Creating a Calculated Field Fundamentals of Calculations The Reference List Syntax Description Example Building the Calculation When Calculations Go Well When Calculations Go Poorly Editing Calculated Fields Recommendations Types of Calculations Numerical Calculations String Calculations Date Calculations Conditional Calculations with a Boolean Output Logical Calculations Type Conversions Level of Detail and Ranking Calculations Summary IV. Output 19. Choosing an Output Types of Output Publish to Files Publish to Tableau Server When to Output Data in Prep Builder Outputting Data in the Output Step Previewing Output Data in Desktop Other Considerations for Output Data Summary 20. Outputting to a Database When to Write to a Database Clean Data Simplified Joins Staging and Reference Tables Setup for Writing to a Database What to Watch Out For Summary 21. Getting Started with Tableau Prep Conductor When to Use Prep Conductor How to Get Prep Conductor Loading a Flow to Prep Conductor Other Benefits of Using Prep Conductor Summary V. Cleaning Data 22. Creating Additional Data When Not to Create Data Dynamic Calculations in Desktop Duplicate Records from Joins Creating Additional Columns Using Calculations Pivoting Rows to Columns Joining Data Sets Creating Additional Rows Pivoting Columns to Rows Unioning Data Sets Scaffolding Data Sets Joining Data Sets Summary 23. Filtering What Is a Filter? Different Types of Filters Selection Calculation Wildcard Null Values When to Filter Out Columns When to Filter Out Rows Summary 24. Removing Data During Input Changing Your Data Set Before Loading It Slow Performance, Slow Build, Slow Output Removing Columns Removing Records Summary 25. Splitting Data Fields Basic Splits Advanced Splits: When Automatic Splits Don’t Work as Intended When Not to Split Data Address Data No Clear Delimiter Summary 26. Cleaning by Grouping Data What Does Grouping Mean? Why Use Grouping Improving Accuracy Navigating the Data Hierarchy Smoothing Reorganizations Grouping Techniques Manual Calculations Built-in Functionality Summary 27. Dealing with Nulls What Is a Null? When Is a Null OK? How to Remove or Replace a Null ISNULL() ZN() Merge Summary 28. Using Data Roles How to Use Data Roles Custom Data Roles Summary 29. Dealing with Unwanted Characters What Is an Unwanted Character? Issues Caused by Unwanted Characters Removing Unwanted Characters Strings with Mistyped Characters Numbers with Unwanted Characters Dates with Mistyped Characters Summary 30. Deduplicating How to Identify Duplicates Causes of Duplicates System Loads Row per Measure Joins How to Handle Duplicates Aggregating: Technique 1 Aggregating: Technique 2 Pivoting Rows to Columns Summary 31. Using Regular Expressions What Are Regular Expressions? How to Use Regexes in Prep REGEXP_EXTRACT() and REGEXP_EXTRACT_NTH() REGEXP_MATCH() REGEXP_REPLACE() Regex Use Cases Replacing Common Mistakes Anonymizing Comments or Feedback Common Regex Commands Summary 32. Completing Advanced Joins Multiple Join Conditions Join Conditions Other Than Equals Filtering with a Join Joining by a Range OR Statements Summary 33. Creating Level of Detail Calculations What Is Appending? Exploring Appending Through LOD Calculations When to Use an LOD Calculation How to Write an LOD Calculation in Prep Builder What a Level of Detail Calculation Is Doing Summary 34. Doing Analytical Calculations What Is a Table Calculation? Applying Table Calculation Logic in Prep Builder Keywords Analytical Calculations Use Cases Filtering for the Top N Filtering Out a Percentage of Data Summary VI. Beyond the Basics 35. Breaking Down Complex Data Preparation Challenges The Challenge Where to Begin Logical Steps Making Changes Be Ready to Iterate Summary 36. Handling Free Text What Is Free Text? Why Is Free Text Useful? How to Analyze Free Text in Tableau Split the Strings Pivot Columns to Rows Clean Cases and Punctuation Use a Join to Remove Common Words Group the Remaining Values Summary 37. Using Smarter Filtering Calculations Boolean Calculations Logical Calculations Regex Calculations Join Ranges Percentage Variance Manual Entry: Level of Detail Calculations Reloaded Data: Join to Previous Output Aggregating the Average Production Cost per Type Joining the Data Sets Together Combining Techniques Summary 38. Managing Conversion Rates Challenges of Conversion Rates Applying Conversion Rates in Prep Step 1: Create a Consistent Granularity of Data for the Conversion Step 2: Join the Data Sets Together Step 3: Apply the Conversion Rate Long-Term Strategies for Conversion Rates Managing Frequency Maintaining History Tables Summary 39. Scaffolding Your Data What Is Scaffolding? Challenges Addressed by Scaffolding Challenges Created by Scaffolding The Traditional Scaffolding Technique Step 1: Input the Data Sets Step 2: Build the Join Calculations Step 3: Join the Two Data Sets Together Step 4: Filter Out Unnecessary Rows The Newer Scaffolding Technique Step 1: Input the Data Sets Step 2: Join the Data Sets Step 3: Add the Reporting Date Step 4: Remove the Scaffold Value The Result Summary 40. Connecting to Programming Scripts When to Use the Script Step in Prep Setting Up Your Computer to Use Scripts in Prep Using a Script Step Summary 41. Handling Prep Builder Errors Parameter Errors Blank Profile Panes or Data Panes Changing a Calculation or Removing a Data Field Downstream The Data Source Has Changed Errors Within a Calculated Field Incomplete Calculations Unsupported Functions Summary VII. Managing Your Data 42. Documenting Your Data Preparation Basic Documentation Folder Structure Filenames Data Sources Output Step Names Clean Step Step Descriptions Color Joins Unions Summary 43. Deciding Where to Prepare Your Data Processes to Consider Data Preparation Versus Visual Analytics Data Literacy Organization Size Quality of Technological Hardware History of Data Investment Software Performance Sampling Functionality Documentation Summary 44. Managing Data What Is Sensitive Data? Public Confidential Strictly Confidential Restricted Managing Data Based on Sensitivity Production Versus Development Environments Deleting Data When Data Becomes Outdated or Irrelevant When a Customer or Client Leaves Summary 45. Storing Your Data Inaccessibility Don’t Break the Law Don’t Delete Operational Data Do Grant Access to Data for the Experts Do Document Your Sources Slow/Unresponsive Performance Overwriting Risks Grant Read-Only Access Train Before Publishing So, Where Do You Write That Output? Summary 46. Using Identifiers and Keys in Data What Is an Identifier? What Is a Key in a Database? Using Keys and Identifiers in Prep Creating Identifier Data Fields in Prep Builder Summary 47. Keeping Your Data Up-to-Date Refreshing Data Full Versus Incremental Refreshes Setting Up Different Types of Refresh Full Refresh Incremental Refresh What to Watch Out for When Refreshing Data Sources Changing Data Values Altering the Structure of Sources New Data, New Input Summary 48. Using History Tables Why Are History Tables Required? What to Consider When Creating History Tables Ability to Join to Live Data Relevance of Information Frequency of Updates Level of Granularity Performance Data Regulations An Example History Table Summary 49. Evaluating Whether You Need Prep Builder at All A History of Data Preparation in Tableau Where to Try Desktop First Simple Joins Unions Single Pivots Where to Start with Prep Builder Summary 50. Final Thoughts Index
520			_aFor self-service data preparation, Tableau Prep is relatively easy to use—as long as you know how to clean and organize your datasets. Carl Allchin, from The Information Lab in London, gets you up to speed on Tableau Prep through a series of practical lessons that include methods for preparing, cleaning, automating, organizing, and outputting your datasets. Based on Allchin’s popular blog, Preppin’ Data, this practical guide takes you step-by-step through Tableau Prep’s fundamentals. Self-service data preparation reduces the time it takes to complete data projects and improves the quality of your analyses. Discover how Tableau Prep helps you access your data and turn it into valuable information. Know what to look for when you prepare data Learn which Tableau Prep functions to use when working with data fields Analyze the shape and profile of your dataset Output data for analysis and learn how Tableau Prep automates your workflow Learn how to clean your dataset using Tableau Prep functions Explore ways to use Tableau Prep techniques in real-world scenarios Make your data available to others by managing and documenting the output. taken from Publisher's site.
630	0	0	_aTableau (Computer file)
650		0	_aInformation visualization _xComputer programs.
856			_3Publisher's Description and Content page _uhttps://www.oreilly.com/library/view/tableau-prep-up/9781492079613/
942			_2ddc _cM
999			_c12874 _d12874